Topics in Artificial Intelligence Applied to Industry 4.0 E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Veröffentlichungsjahr: 2024

Beschreibung

Topics in Artificial Intelligence Applied to Industry 4.0 Forward thinking resource discussing emerging AI and IoT technologies and how they are applied to Industry 4.0 Topics in Artificial Intelligence Applied to Industry 4.0 discusses the design principles, technologies, and applications of emerging AI and IoT solutions on Industry 4.0, explaining how to make improvements in infrastructure through emerging technologies. Providing a clear connection with different technologies such as IoT, Big Data, AR and VR, and Blockchain, this book presents security, privacy, trust, and other issues whilst delving into real-world problems and case studies. The text takes a highly practical approach, with a clear insight on how readers can increase productivity by drastically shortening the time period between the development of a new product and its delivery to customers in the market by 50%. This book also discusses how to save energy across systems to ensure competitiveness in a global market, and become more responsive in how they produce products and services for their consumers, such as by investing in flexible production lines. Written by highly qualified authors, Topics in Artificial Intelligence Applied to Industry 4.0 explores sample topics such as: * Quantum machine learning, neural network implementation, and cloud and data analytics for effective analysis of industrial data * Computer vision, emerging networking technologies, industrial data spaces, and an industry vision for 2030 in both developing and developed nations * Novel or improved nature-inspired optimization algorithms in enhancing Industry 5.0 and the connectivity of any components for smart environment * Future professions in agriculture, medicine, education, fitness, R&D, and transport and communication as a result of new technologies Aimed at researchers and students in the interdisciplinary fields of Smart Manufacturing and Smart Applications, Topics in Artificial Intelligence Applied to Industry 4.0 provides the perfect overview of technology from the perspective of modern society and operational environment.

Leseprobe

Table of Contents

Cover

Table of Contents

Title Page

Copyright Page

About the Editors

List of Contributors

Preface

Acknowledgment

1 Introduction to Industry 4.0 and Its Impacts on Society

1.1 Introduction

1.2 The Technological Advancements of the Fourth Industrial Revolution

1.3 Impacts on the Economy

1.4 Impacts on Society

1.5 Ethics and Governance

1.6 Future Directions

1.7 Conclusion

References

2 Digital Transformation Using Industry 4.0 and Artificial Intelligence

2.1 Introduction

2.2 Industry 4.0 Technologies

2.3 AI Features in Industry 4.0

2.4 Industry 4.0 and XAI

2.5 Industry 4.0 Integration Using an XAI‐Based Methodology with AI

2.6 Case Studies for Industry 4.0

2.7 Challenges of Industry 4.0

2.8 Advantages of Intelligent Factory

2.9 Discussion and Emerging Trends

2.10 Conclusion

References

3 Industry 4.0: Design Principles, Challenges, and Applications

3.1 Introduction

3.2 Organization of Chapter

3.3 Industrial Revolutions

3.4 Generations of Industrial Revolutions

3.5 Transformation to Industry 4.0

3.6 Characteristics of Industry 4.0

3.7 Technologies Under Industry 4.0

3.8 Design Principles of Industry 4.0

3.9 Applications of Industry 4.0

3.10 Trends in Industry 4.0

3.11 Challenges of Industry 4.0

3.12 Related Works

3.13 Paradigm Shift Toward Industry 5.0

3.14 Future Challenges and Research

3.15 Conclusion

References

4 Detection from Chest X‐Ray Images Based on Modified Deep Learning Approach

4.1 Introduction

4.2 Related Works

4.3 Research Methodology

4.4 Results and Discussions

4.5 Conclusions

References

5 Smart Technologies in Manufacturing Industries: A Useful Perspective

5.1 Introduction

5.2 Literature Review

5.3 Materials and Methods

5.4 Discussion

5.5 Conclusion

References

6 Blockchain Technology for Industry 4.0

6.1 Introduction

6.2 Key Concepts of Blockchain

6.3 Blockchain in Data Privacy and Security

6.4 Cybersecurity in the Era of Industry 4.0

6.5 Supply Chain Management and Traceability

6.6 Blockchain‐Enabled Smart Manufacturing

6.7 Overcoming Challenges in Blockchain Implementation

6.8 Real‐World Applications of Blockchain in Industry 4.0

6.9 Future Trends

6.10 Conclusion

Declarations

References

7 Unifying Technologies in Industry 4.0: Harnessing the Synergy of Internet of Things, Big Data, Augmented Reality/ Virtual Reality, and Blockchain Technologies

7.1 Introduction to Industry 4.0

7.2 Internet of Things

7.3 Big Data

7.4 Augmented Reality and Virtual Reality

7.5 Blockchain

7.6 Convergence of IoT, Big Data, AR/VR, and Blockchain in Industry 4.0

7.7 Conclusion

References

8 Industry 4.0 in Manufacturing, Communication, Transportation, and Health Care

8.1 Introduction

8.2 Diversified Applications of Industry 4.0

8.3 Conclusion

References

9 Transforming Education Management in the Industry 4.0 Era: Harnessing the Power of Cloud‐Based Blockchain

9.1 Introduction

9.2 Revolutionizing Education Through Technology: The Power of Innovation and Connectivity

9.3 Blockchain Application in Education with Industry 4.0: Revolutionizing Learning, Credentialing, and Collaboration

9.4 Blockchain Solution Providers for Education in the Era of Industry 4.0

9.5 Navigating the Challenges: Implementing Blockchain in Education Within the Industry 4.0 Landscape

9.6 A Vision for the Future

9.7 Conclusion

References

10 Future Professions in Agriculture, Medicine, Education, Fitness, Research and Development, Transport, and Communication

10.1 Introduction

10.2 Literature Review

10.3 AI Impact on Future Professions

10.4 Role Model of AI in Industry 4.0

10.5 AI in Agriculture

10.6 AI in Medicine

10.7 Role of AI in Challenges of Medicine

10.8 AI in Education

10.9 AI in Fitness

10.10 AI in R&D

10.11 AI in Transport

10.12 AI Market Growth in Future Profession

10.13 Conclusion

References

11 Cybersecurity Issues and Challenges in Quantum Computing

11.1 Introduction

11.2 Cybersecurity Issues and Challenges in Quantum Computer

11.3 General Solutions

11.4 Conclusion

References

12 Security, Privacy, Trust, and Other Issues in Industries 4.0

12.1 Introduction

12.2 Security Fog Computing

12.3 IoT Challenges

12.4 Security Threats and Solutions of Industrial Internet of Things

12.5 Conclusion

References

13 Designing a Quantum Computer to Gear up Artificial Intelligence for Industry 4.0

13.1 Introduction

13.2 Literature Survey

13.3 Proposed Work

13.4 Simulation Results

13.5 Conclusion and Future Work

References

14 Opportunities in Neural Networks for Industry 4.0

14.1 Introduction: Why Is Machine Learning Interesting to Industry 4.0?

14.2 Machine Learning

14.3 Challenges in Industry 4.0 That Can Benefit from Using Machine Learning

14.4 Some Cases of Success Deploying ML in Industry 4.0

14.5 Conclusions and Final Remarks

References

15 A Smarter Way to Collect and Store Data: AI and OCR Solutions for Industry 4.0 Systems

15.1 Introduction

15.2 Background

15.3 Architecture of Wireless Extraction of Display Panel

15.4 ESP32 Cam Module

15.5 Wireless LAN Network Setup

15.6 Optical Character Recognition for Text Detection and Text Recognition

15.7 Working of the Model

15.8 Application GUI

15.9 Conclusion

References

Index

End User License Agreement

List of Tables

Chapter 2

Table 2.1 An overview of recent cutting‐edge technologies, which enable the...

Table 2.2 An overview of the various AI with XAI techniques applied in diff...

Table 2.3 An outline of the various AI with XAI‐based techniques in differe...

Table 2.4 Industry 4.0 in conjunction with extensions of AI and XAI‐based t...

Table 2.5 Challenges of Industry 4.0 versus traditional industry.

Chapter 3

Table 3.1 Summary of Industry 4.0 challenges.

Chapter 4

Table 4.1 Non‐TB and TB images in a different dataset.

Table 4.2 Confusion matrix.

Table 4.3 U‐Net model hyperparameters.

Table 4.4 Initial model assessment for hyperparameter selection.

Table 4.5 Hyperparameters for model training.

Table 4.6 U‐Net model performance.

Table 4.7 Performance analysis of EfficientNet‐B0.

Table 4.8 Performance analysis of ChexNet.

Table 4.9 Performance summary of SqueezeNet classifier.

Table 4.10 Sum of probabilities ensemble.

Table 4.11 Performance of stacked generalization ensemble models.

Table 4.12 Performance comparison for the methodology components analysis....

Table 4.13 Comparison of performance on the TBX11K dataset.

Table 4.14 Performance comparison with other TB classification models.

Chapter 5

Table 5.1 Abbreviation of technologies used in manufacturing sectors.

Table 5.2 Survey theme.

Table 5.3 Survey report of MHI in 2018.

Chapter 8

Table 8.1 Mutual features of I4.0.

Table 8.2 Comparison of IoT protocols.

Chapter 9

Table 9.1 Overview of literature on blockchain applications in education.

Chapter 13

Table 13.1 Qubit category‐wise proportion.

Table 13.2 Cold versus hot qubit proportion combinations.

Table 13.3 Actual number of cold versus hot qubit combinations.

Table 13.4 Quantum datasets for the various qubits.

Table 13.5 Quantum datasets sorted based on magnitude.

Table 13.6 Quantum datasets ranked based on the magnitude.

Table 13.7 Qubit categorization and range with a mid‐value bound.

Table 13.8 Comparison of processing times (simple versus complex datasets)....

Table 13.9 Comparison of memory consumption (simple vs complex datasets).

Chapter 14

Table 14.1 Summary of firmware versions used in the experiments: operationa...

Table 14.2 Generated samples for experimental validation and respective amo...

List of Illustrations

Chapter 1

Figure 1.1 Major technological advancements driving the revolution.

Figure 1.2 Importance of studying and understanding the impacts of the Fourt...

Figure 1.3 Potential benefits.

Figure 1.4 Impacts on the economy.

Figure 1.5 Potential impacts of the Fourth Industrial Revolution on the econ...

Figure 1.6 Fourth Industrial Revolution.

Figure 1.7 Impacts on society.

Figure 1.8 Ethics and governance.

Chapter 2

Figure 2.1 The four decades of industrialization are depicted schematically....

Figure 2.2 Support of key technologies in Industry 4.0.

Figure 2.3 Areas of Industry 4.0 with a focus on AI.

Figure 2.4 An Outline of XAI‐based techniques.

Figure 2.5 Industry 4.0 framework and contributing digital technologies.

Chapter 3

Figure 3.1 Industrial revolutions.

Figure 3.2 Advancements during Industry 1.0.

Figure 3.3 Advancements during Industry 2.0.

Figure 3.4 Advancements during Industry 3.0.

Figure 3.5 Characteristics of Industry 4.0.

Figure 3.6 Technologies under Industry 4.0.

Figure 3.7 Cyber‐physical system – a conceptual diagram.

Figure 3.8 Internet of Things – a conceptual diagram.

Figure 3.9 Cloud technology architecture.

Figure 3.10 Artificial intelligence technologies.

Figure 3.11 Blockchain applications.

Figure 3.12 Application areas of visualization technologies.

Figure 3.13 Application areas of automation and industrial robots.

Figure 3.14 Application areas of additive manufacturing.

Figure 3.15 Design principles of Industry 4.0.

Figure 3.16 Industry 4.0 application.

Figure 3.17 Industry 4.0 market size 2023–2030 (US$ in billions).

Figure 3.18 Industry 4.0 challenges.

Figure 3.19 Industry 5.0 characteristics.

Chapter 4

Figure 4.1 U‐Net model.

Figure 4.2 High‐emphasis filter image creation flow.

Figure 4.3 Bilateral filter, CLAHE, HEF of the original image.

Figure 4.4 Different data augmentation images.

Figure 4.5 Phases to fine‐tune a CNN model.

Figure 4.6 Parameters for the ChexNet model.

Figure 4.7 Training parameters of EfficientNet‐B0.

Figure 4.8 Squeeze and expand the layer of the SqueezeNet model.

Figure 4.9 SqueezeNet params.

Figure 4.10 Sum of probabilities ensemble.

Figure 4.11 Stacked generalization ensemble.

Figure 4.12 Samples for image enhancements.

Figure 4.13 Predicted masks of the U‐Net model.

Figure 4.14 Original X‐ray, segmented, and dilated lung X‐rays.

Figure 4.15 Confusion matrix obtained using SOP ensemble model on TBX11K dat...

Figure 4.16 Confusion matrix for MC and Shenzhen dataset.

Chapter 5

Figure 5.1 Steps to produce ST.

Figure 5.2 Smart technology.

Figure 5.3 Five architecture of cyber‐physical production system.

Figure 5.4 How CPPS system works.

Figure 5.5 Some types of sensors.

Chapter 6

Figure 6.1 Evolution of Industry 4.0.

Figure 6.2 Industry 4.0 key technologies.

Figure 6.3 Various applications of blockchain.

Figure 6.4 Key characteristics of blockchain technology.

Figure 6.5 Smart contracts.

Figure 6.6 Public versus. private blockchain.

Chapter 7

Figure 7.1 Components and technologies of Industry 4.0.

Figure 7.2 Industry 4.0 architecture.

Figure 7.3 Concept of IoT.

Figure 7.4 Big data characteristics.

Figure 7.5 AR architecture.

Figure 7.6 VR architecture.

Figure 7.7 Architecture of blockchain.

Figure 7.8 Interplay and integration of technologies.

Figure 7.9 Smart manufacturing.

Figure 7.10 Supply chain management.

Chapter 8

Figure 8.1 Sectors of IR4.0.

Figure 8.2 Industrial evolution era.

Figure 8.3 Fundamental approaches of I4.0.

Figure 8.4 Interaction between CPS and IIoT.

Figure 8.5 Industry 4.0 facilitating technologies.

Figure 8.6 I4.0 envisions.

Figure 8.7 System architecture of I4.0.

Figure 8.8 Interaction between smart factories and consumers in I4.0.

Figure 8.9 Smart factory reference architecture.

Figure 8.10 Strategy for enhancing the smart organization power control thro...

Figure 8.11 RAMI 4.0 model.

Figure 8.12 Communication protocols: (a) IEEE, (b) HTTP, and (c) CoAP.

Figure 8.13 Digitalization in health care.

Chapter 9

Figure 9.1 Application of blockchain in education.

Figure 9.2 Blockchain applications and solutions.

Figure 9.3 The potential of blockchain in higher education.

Chapter 10

Figure 10.1 AI applications.

Figure 10.2 AI roadmap for future professions.

Figure 10.3 Agriculture improvement areas promised through data extraction....

Figure 10.4 Applying AI in agriculture.

Figure 10.5 Medicine improvement areas promised through data extraction.

Figure 10.6 Applying AI in medicine.

Figure 10.7 Most prevalent AI challenges in the field of medicine.

Figure 10.8 AI employment in education.

Figure 10.9 Most prevalent AI challenges in the profession of education.

Figure 10.10 Applying AI in fitness.

Figure 10.11 Most prevalent AI challenges in the field of fitness.

Figure 10.12 Impact of AI in R&D.

Figure 10.13 AI market growth in the future profession.

Chapter 11

Figure 11.1 Some cybersecurity issues and challenges in quantum computing.

Figure 11.2 Cybersecurity issues in quantum computing.

Figure 11.3 Quantum challenges with respect to cybersecurity.

Figure 11.4 Challenges and issues faced by a QKD and its optimal respective ...

Figure 11.5 Classification of PQC challenges.

Figure 11.6 AShor algorithm‐based quantum attack.

Figure 11.7 Some general algorithmic issues in quantum computing.

Figure 11.8 Standardization issues, impacts, and its probable solutions.

Figure 11.9 Quantum‐aware infrastructure.

Figure 11.10 Building quantum‐safe technologies.

Chapter 12

Figure 12.1 Industry 4.0 transformation.

Figure 12.2 Globalization 4.0.

Figure 12.3 Life cycle of phishing attacks.

Figure 12.4 IoT security threats.

Figure 12.5 Spoofing.

Figure 12.6 Data tampering.

Figure 12.7 Malicious code injection.

Chapter 13

Figure 13.1 Block diagram of a quantum computer.

Figure 13.2 Cryo‐cooling unit for quantum computer.

Figure 13.3 Qubit functional unit.

Figure 13.4 Quantum data plane.

Figure 13.5 Quantum control plane.

Figure 13.6 Quantum ALU.

Figure 13.7 Quantum memory unit.

Figure 13.8 Quantum display unit.

Figure 13.9 Output of IBM Qiskit illustrating the control and data path sign...

Figure 13.10 Plot comparing the processing time (simple versus complex datas...

Figure 13.11 Plot showing the reduction in processing time for complex datas...

Figure 13.12 Plot comparing the memory consumption (simple versus complex da...

Figure 13.13 Reduction in memory consumption for complex datasets compared t...

Chapter 14

Figure 14.1 Fault detection pipeline.

Figure 14.2 Anomaly detection system architecture. If ∣

ImaA

 − 

ImaB

 ∣  > 

μ

...

Figure 14.3 Experimental prototype developed to validate our research. Devic...

Figure 14.4 Average detection error rate as a function of the decision thres...

Figure 14.5 Minimum detection error rate as a function of the acquisition wi...

Chapter 15

Figure 15.1 A block diagram.

Figure 15.2 A modular diagram.

Figure 15.3 ESP32‐CAM module.

Figure 15.4 ESP32‐CAM pin diagram [10].

Figure 15.5 FTDI programmer and ESP32 camera module.

Figure 15.6 IP MAC binding.

Figure 15.7 Schematic illustration of CRAFT architecture [13].

Figure 15.8 Evaluation metrics.

Figure 15.9 EasyOCR architecture diagram.

Figure 15.10 Character error rate.

Figure 15.11 Detecting of ROI on power supply display unit.

Figure 15.12 Detecting of ROI on power supply display unit.

Figure 15.13 Detection values from display unit stored in csv format.

Figure 15.14 Detection values from display unit stored in csv format.

Figure 15.15 Detection of ROI on function generator display unit.

Figure 15.16 Detection values are stored in csv format from function generat...

Figure 15.17 Home screen.

Figure 15.18 Entering stream link.

Figure 15.19 Waiting message before showing the stream.

Figure 15.20 Machine display.

Figure 15.21 ROI detection by algorithm.

Figure 15.22 Manual selection of ROI.

Figure 15.23 Detected results.

Guide

Cover Page

Table of Contents

Title Page

Copyright Page

About the Editors

List of Contributors

Preface

Acknowledgment

Begin Reading

Index

WILEY END USER LICENSE AGREEMENT

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Topics in Artificial Intelligence Applied to Industry 4.0

Edited by

Mahmoud Ragab AL‐Refaey

Information Technology Department, Faculty of Computing and Information Technology (FCIT), King Abdulaziz University (KAU), Jeddah, Saudi Arabia

Mathematics Department, Faculty of Science, Al‐Azhar University, Naseir City, Cairo, Egypt

Amit Kumar Tyagi

Department of Fashion Technology, National Institute of Fashion Technology, New Delhi, India

Abdullah Saad AL‐Malaise AL‐Ghamdi

Information Systems Department, Faculty of Computing and Information Technology (FCIT), King Abdulaziz University (KAU), Jeddah, Saudi Arabia

Information Systems Department, School of Engineering, Computing and Design, Dar Al‐Hekma University, Jeddah, Saudi Arabia

Swetta Kukreja

Department of Computer Science and Engineering, Amity University, Mumbai, Maharashtra, India

This edition first published 2024© 2024 John Wiley & Sons Ltd

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About the Editors

Mahmoud Ragab AL‐Refaey obtained his Ph.D. degree from the Faculty of Mathematics and Natural Sciences at the Christian‐Albrechts‐University in Kiel (CAU), Schleswig‐Holstein, Germany.

He received his B.SC. degree in Statistics Computer Science from Mansoura University in Mansoura, Egypt. He is a professor of data science at the Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University in Jeddah, Saudi Arabia and the Mathematics Department, Faculty of Science, Al Azhar University in Cairo, Egypt. He worked in different research groups at various universities such as the Combinatorial Optimization and Graph Algorithms Group (COGA), Faculty of Mathematics and Natural Sciences, Berlin University of Technology in Berlin, Germany; Faculty of Informatics and Computer Science at the British University in Egypt BUE; Integrated Communication Systems Group at Ilmenau University of Technology TU Ilmenau, in Thüringen, Germany. Now he is a researcher at various centers such as: University of Oxford Centre for Artificial Intelligence in Precision medicines; Center of Research Excellence in Artificial Intelligence and Data Science; Center of Excellence in Smart Environment Research at King Abdulaziz University, Jeddah, Saudi Arabia. He has published over 100 papers in refereed high‐impact journals, books, and patents. His research focuses on: AI, Deep learning, Optimization, Mathematical Modeling, Data Science, Neural Networks, Time series analysis, and decision support systems.

Amit Kumar Tyagi is working as an assistant professor at the National Institute of Fashion Technology, New Delhi, India. Previously, he worked as an assistant professor (Senior Grade 2) and senior researcher at Vellore Institute of Technology (VIT), Chennai Campus, Chennai, Tamil Nadu, India, for the period of 2019–2022. He received his PhD degree (full‐time) in 2018 from Pondicherry Central University, Puducherry, India. Regarding his academic experience, he joined Lord Krishna College of Engineering, Ghaziabad (LKCE) for the periods of 2009–2010 and 2012–2013. He was an assistant professor and head of research at Lingaya’s Vidyapeeth (formerly known as Lingaya’s University), Faridabad, Haryana, India, for the period of 2018–2019. His supervision experience includes more than 10 master’s dissertations and one PhD thesis. He has contributed to several projects such as AARIN and P3‐ Block to address some of the open issues related to privacy breaches in vehicular applications (such as parking) and medical cyber‐physical systems (MCPS). He has published over 100 papers in refereed high‐impact journals, conferences, and books, with some of his articles receiving best paper awards. Also, he has filed more than 20 patents (nationally and internationally) in the areas of deep learning, the Internet of Things, cyber‐physical systems, and computer vision. He has edited more than 20 books for IET, Elsevier, Springer, CRC Press, and so on. Furthermore, he has authored three books on the Internet of Things, intelligent transportation systems, and vehicular ad hoc networks with BPB Publication, Springer, and IET publisher, respectively. He is a winner of the Faculty Research Award for the years 2020, 2021, and 2022 (consecutively three years), given by Vellore Institute of Technology, Chennai, India. Recently, he has received the best paper award for a paper titled “A Novel Feature Extractor Based on the Modified Approach of Histogram of Oriented Gradient” at ICCSA 2020 in Italy, Europe. His current research focuses on next‐generation machine‐based communications, blockchain technology, smart and secure computing, and privacy. He is a regular member of the ACM, IEEE, MIRLabs, Ramanujan Mathematical Society, Cryptology Research Society, and Universal Scientific Education and Research Network, CSI, and ISTE.

Abdullah Saad AL‐Malaise AL‐Ghamdi is a professor of software & systems Engineering and AI, associated with Faculty of Computing and Information Technology (FCIT) at King Abdulaziz University (KAU), Jeddah, Saudi Arabia. He is a professor at the Information Systems Department, School of Engineering, Computing and Design, Dar Al‐Hekma University, in Jeddah, Saudi Arabia. He received his PhD. degree in computer science from George Washington University, USA, in 2003. He is a member of the Scientific Council and holds the position of secretary general of the scientific council at KAU. In addition, he is working as the head of Consultant’s unit at the Vice‐President for Development Office, as a consultant to the vice‐president for Graduate Studies & Scientific Research at KAU. Previously, he has worked as the head of the IS Department, vice dean for Graduate Studies and Scientific Research, and head of the Computer Skills Department at FCIT. Recently he is a researcher at various centers such as: University of Oxford Centre for Artificial Intelligence in Precision Medicines; Center of Research Excellence in Artificial Intelligence and Data Science; Center of Excellence in Smart Environment Research at King Abdulaziz University, Jeddah, Saudi Arabia. He has supervised many MSc & PhD students who are now successful in and outside academia. He has published many papers in refereed high‐impact journals, books, and patents. His main research areas are software engineering and systems, artificial intelligence, data analytics, business intelligence, and decision support systems.

Swetta Kukreja is working as an associate professor in the Department of CSE at Amity University, Mumbai. She has more than 10 years of teaching and research experience. She has completed her PhD from Lingaya’s University, Faridabad. She had served as an editor for many international conferences and journals. She has many publications (including patents) in national and international conferences and journals and has also served as a reviewer for the same. She is a member of ACM and IEEE.

List of Contributors

Monika AgarwalComputer Science and EngineeringDayananda Sagar UniversityBengaluru, Karnataka, India

Abdullah Saad AL‐Malaise AL‐GhamdiInformation Systems Department, Faculty of Computing and Information Technology (FCIT)King Abdulaziz University (KAU)Jeddah, Saudi Arabia

Information Systems DepartmentSchool of Engineering, Computing and DesignDar Al‐Hekma UniversityJeddah, Saudi Arabia

K. AnnamalaiVIT UniversityChennai, Tamil Nadu, India

Nusrat J. AnsariComputer Science DepartmentVivekanand Education Society's Institute of TechnologyMumbai, Maharashtra, India

Dinesh Kumar AtalDepartment of Biomedical EngineeringDeenbandhu Chhotu Ram University of Science and TechnologySonipat, Haryana, India

Ananda K. BeheraArtificial Intelligence and Machine Learning ProgrammeLiverpool John Moores UniversityLiverpool, UK

Rajiv Kumar BerwerDepartment of Computer Science and EngineeringDeenbandhu Chhotu Ram University of Science and TechnologySonipat, Haryana, India

Biswajit R. BhowmikBRICS LaboratoryDepartment of Computer Science and Engineering National Institute of Technology KarnatakaMangalore, Karnataka, India

Sovers Singh BishtNoida Institute of Engineering and TechnologyGreater Noida, Uttar Pradesh, India

Priyanka ChandaniNoida Institute of Engineering and TechnologyGreater Noida, Uttar Pradesh, India

Mani D. ChoudhryDepartment of Information TechnologyKGiSL Institute of TechnologyCoimbatore, Tamil Nadu, India

Jyoti DabassDBT Centre of Excellence Biopharmaceutical Technology, IITNew Delhi, India

Manju DabassEECE DepartmentThe Northcap UniversityGurugram, Haryana, India

José P.G. de OliveiraPolytechnic School of PernambucoUniversity of PernambucoRecife, Pernambuco, Brazil

M.K. DharaniDepartment of AIKongu Engineering CollegeErode, Tamil Nadu, India

N. EthirajDr. M.G.R. Educational and Research InstituteChennai, Tamil Nadu, India

Carmelo J.A.B. FilhoPolytechnic School of PernambucoUniversity of PernambucoRecife, Pernambuco, Brazil

S. GeethaDr. M.G.R. Educational and Research InstituteChennai, Tamil Nadu, India

K.K. GirishBRICS LaboratoryDepartment of Computer Science and Engineering National Institute of Technology KarnatakaMangalore, Karnataka, India

V.M. GobinathRajalakshmi Institute of TechnologyChennai, Tamil Nadu, India

M. GunasekarDepartment of Information TechnologyKongu Engineering CollegeErode, Tamil Nadu, India

Sanjeev IndoraDepartment of Computer Science and EngineeringDeenbandhu Chhotu Ram University of Science and TechnologySonipat, Haryana, India

Garima JainNoida Institute of Engineering and TechnologyGreater Noida, Uttar Pradesh, India

Guru Akaash N. JanthalurComputer Science DepartmentVivekanand Education Society's Institute of TechnologyMumbai, Maharashtra, India

A.S. JayasuryaDepartment of Electrical and ElectronicsUniversiti Teknologi PetronasPerak, Malaysia

G. Belshia JebamalarDepartment of Computer Science and EngineeringS.A. Engineering CollegeChennai, Tamil Nadu, India

Manoj JoshiDepartment of ECEJSS Academy of Technical EducationNoida, Uttar Pradesh, India

Igor JurcicTelecommunications and Informatics DepartmentHT ERONETMostar, Bosnia and Herzegovina

S.K. Rajesh KannaRajalakshmi Institute of TechnologyChennai, Tamil Nadu, India

Vinod M. KapseNoida Institute of Engineering and TechnologyGreater Noida, Uttar Pradesh, India

Manigandan KashimaniComputer Science DepartmentVivekanand Education Society's Institute of TechnologyMumbai, Maharashtra, India

A. KathirvelPanimalar Engineering CollegeChennai, Tamil Nadu, India

M. KeerthikaDepartment of Computer Science and EngineeringRajalakshmi Engineering CollegeChennai, Tamil Nadu, India

Utku KöseComputer EngineeringSuleyman Demirel UniversityKaskelen, Kazakhstan

Swetta KukrejaDepartment of Computer Science and EngineeringAmity UniversityMumbai, Maharashtra, India

Ambeshwar KumarComputer Science and EngineeringGITAM UniversityVisakhapatnam, Andhra Pradesh, India

K. Pradheep KumarDepartment of CSISBITS PilaniPilani, Rajasthan, India

K.R. Prasanna KumarDepartment of Information TechnologyKongu Engineering CollegeErode, Tamil Nadu, India

Sunil KumarBRICS LaboratoryDepartment of Computer Science and Engineering National Institute of Technology KarnatakaMangalore, Karnataka, India

R. Lalitha PriyaComputer Science DepartmentVivekanand Education Society's Institute of TechnologyMumbai, Maharashtra, India

K. LogeswaranDepartment of AIKongu Engineering CollegeErode, Tamil Nadu, India

M. ManjulaComputer Science and EngineeringDayananda Sagar UniversityBengaluru, Karnataka, India

Bireshwar D. MazumdarDepartment of Computer Science and EngineeringFaculty of Engineering and TechnologyUnited University PrayagrajAllahabad, Uttar Pradesh, India

K. MehataDr. M.G.R. Educational and Research InstituteChennai, Tamil Nadu, India

Rodrigo de Paula MonteiroPolytechnic School of PernambucoUniversity of PernambucoRecife, Pernambuco, Brazil

Sundarrajan MunusamyDepartment of Networking and CommunicationsSRM Institute of Science & TechnologyChennai, Tamil Nadu, India

Parimala D. MuthusamyDepartment of Electronics and Communication EngineeringVelalar College of Engineering and TechnologyErode, Tamil Nadu, India

Ajay R. NairComputer Science DepartmentVivekanand Education Society's Institute of TechnologyMumbai, Maharashtra, India

Kanchan NaithaniSchool of Computing Science and EngineeringGalgotias UniversityGreater Noida, Uttar Pradesh, India

Sérgio C. OliveiraPolytechnic School of PernambucoUniversity of PernambucoRecife, Pernambuco, Brazil

Ts. Sundaresan PerumalUniversiti Sains Islam MalaysiaBandar Baru NilaiNegeri Sembilan, Malaysia

PoojaComputer Science and EngineeringDayananda Sagar UniversityBengaluru, Karnataka, India

M. PragadeeshDepartment of Information TechnologyRajalakshmi Engineering CollegeChennai, Tamil Nadu, India

Soniya PriyatharsiniDr. M.G.R. Educational and Research InstituteChennai, Tamil Nadu, India

Mahmoud Ragab AL‐RefaeyInformation Technology Department, Faculty of Computing and Information Technology (FCIT)King Abdulaziz University (KAU)Jeddah, Saudi Arabia

Mathematics DepartmentFaculty of ScienceAl‐Azhar UniversityNaseir City, Cairo, Egypt

R. RahulDr. M.G.R. Educational and Research InstituteChennai, Tamil Nadu, India

R. RajadeviDepartment of AIKongu Engineering CollegeErode, Tamil Nadu, India

Manikandan RamachandranSchool of ComputingSASTRA Deemed UniversityThanjavur, Tamil Nadu, India

M. SanthiyaDepartment of Computer Science and EngineeringRajalakshmi Engineering CollegeChennai, Tamil Nadu, India

V. SaravananDepartment of Computer ScienceCollege of Engineering and TechnologyDambi Dollo UniversityDambi Dollo, Oromia Region, Ethiopia

S. SavithaDepartment of CSEK.S.R. College of EngineeringTiruchengode, Tamil Nadu, India

Juergen SeitzDuale Hochschule Baden‐WürttembergWirtschaftsinformatik, Heidenheim, Germany

S. SendilvelanDr. M.G.R. Educational and Research InstituteChennai, Tamil Nadu, India

Neha SharmaTata Consultancy ServicesPune, Maharashtra, India

Arun K. SinghDepartment of Computer Science & EngineeringGreater Noida Institute of TechnologyGreater Noida, Uttar Pradesh, India

Mohan SinghDepartment of ECEG.L. Bajaj Institute of Technology and ManagementGreater Noida, Uttar Pradesh, India

Jeevanandham SivarajDepartment of Information TechnologySri Ramakrishna Engineering CollegeCoimbatore, Tamil Nadu, India

P. SureshDepartment of Database SystemsSchool of Computer Science and EngineeringVellore Institute of TechnologyVellore, Tamil Nadu, India

Shrikant TiwariSchool of Computing Science and EngineeringGalgotias UniversityGreater Noida, Uttar Pradhesh, India

Varun D. TripathyComputer Science DepartmentVivekanand Education Society's Institute of TechnologyMumbai, Maharashtra, India

Amit Kumar TyagiDepartment of Fashion TechnologyNational Institute of Fashion TechnologyNew Delhi, India

Kapil D. TyagiDepartment of ECEJaypee Institute of Information TechnologyNoida, Uttar Pradesh, India

Vaibhav B. TyagiDepartment of ECEISBAT UniversityKampala, Uganda

Harish VenuInstitute of Sustainable Energy (ISE)Universiti Tenaga NasionalPutrajaya Campus, Malaysia

Virendra K. VermaDepartment of Industrial & Production EngineeringInstitute of Engineering and Rural Technology (IERT)Allahabad, Uttar Pradesh, India

Ramesh S. WadawadagiDepartment of Information Science and EngineeringNagarjuna College of EngineeringBengaluru, Karnataka, India

Vivek YadavExpresslending Pty LtdMelbourne, Victoria, Australia

Preface

Industries, as we all know, are the ones that produce goods and services for society. Workers in the textile industry design, fabricate, and sell cloth. The tourist industry includes all the commercial aspects of tourism. The automobile industry makes cars and car parts. The food service industry prepares food and delivers it to hotels, schools, and other big facilities. “Industry” comes from the Latin “industria,” which means “diligence, hard work,” and the word is still used with that meaning. Generally, the industry has been through various evolutions during the last three decades. The industry started in the eighteenth century; that is, in 1784, the first power loom was developed. Hence, Industry 1.0 was all about mechanization with water and steam. In the next phase of the industry revolution, that is, Industry 2.0, the electrification of the industry took place. It started from 1900 to 1950. During this revolution, the “Assembly Line was developed.” Further, Industry 3.0 is about the automation of data. During this revolution, the adoption of computers and automation, enhanced by smart and autonomous systems, is fueled by data and machine learning. All the data that was available manually began to be stored disks. Industry 3.0 was a major revolution in terms of automating things, and even operational technologies came into existence, but there was still a felt need to merge information technology with operational technology to truly digitize the world. This would be called “the Digital Transformation” in the true sense, and the resolution happening in Industry 4.0 is to move toward that direction.

Later, Industry 4.0 is to improve manufacturing efficiency; it is about transforming the way your entire business operates and grows. It is associated with cyber‐physical system, in which digital technologies can create virtual versions of real‐world installations, processes, and applications. This can then be robustly tested to make cost‐effective, decentralized decisions. These virtual copies can then be created in the real world and linked via the Internet of Things (IoT), allowing cyber‐physical systems to communicate and cooperate with each other and human staff to create a joined‐up real‐time data exchange and automation process for Industry 4.0 manufacturing. This should allow for digital transformation and automated and autonomous manufacturing with joined‐up systems that can cooperate with each other. This technology will help solve problems and track processes while increasing productivity. It also primarily focuses on the use of large‐scale machine‐to‐machine communication and IoT deployments to provide increased automation, improved communication, and self‐monitoring, as well as smart machines that can analyze and diagnose issues without the need for human intervention. The idea of connected manufacturing or smart factories is becoming increasingly ubiquitous. Factories and their machines across the globe are getting smarter as connected products and systems operate as part of a larger, more responsive, and agile information infrastructure. The aim is to harvest benefits and improvements in efficiency and profitability, increased innovation, and better management of safety, performance, and environmental impact. This book will provide a complete experience of industrial revolution and its progress toward emerging technology.

Mahmoud Ragab AL‐Refaey

Amit Kumar Tyagi

Abdullah Saad AL‐Malaise AL‐Ghamdi

Swetta Kukreja

Acknowledgment

First of all, we would like to extend our gratitude to our family members, friends, and supervisors, who stood with us as advisors in completing this book. Also, we would like to thank our almighty God, who inspires us to write this book. We also thank Wiley Publishers, who have provided their continuous support all the time, and our colleagues, authors with whom we have worked together inside the college/university and others outside of the college/university, who have provided their continuous support towards completing this book on Topics in Artificial Intelligence Applied to Industry 4.0.

Further, the authors also gratefully acknowledge the support provided by the Faculty of Computing and Information Technology (FCIT) and King Abdulaziz University (KAU), Jeddah, Saudi Arabia, to produce this book. Furthermore, we thank the School of Engineering, Computing and Design, Dar Al‐Hekma University, Jeddah, Saudi Arabia, for their support in performing this book.

We also acknowledge the support provided by the Department of Fashion Technology, National Institute of Fashion Technology, New Delhi, and the Department of Computer Science and Engineering, Amity University Mumbai, India.

Lastly, we would like to thank our respected madam Prof. G. Aghila, Prof. Siva Sathya, Manisha Kinnu (IRS), our respected sir Prof. N Sreenath, and Prof. Aswani Kumar Cherukuri for giving their valuable inputs and helping us in completing this book with Wiley Publisher.

Once again, thanks to all.

Mahmoud Ragab AL‐Refaey

Amit Kumar Tyagi

Abdullah Saad AL‐Malaise AL‐Ghamdi

Swetta Kukreja

1Introduction to Industry 4.0 and Its Impacts on Society

Shrikant Tiwari1, Kanchan Naithani1, Arun K. Singh2, Virendra K. Verma3, Ramesh S. Wadawadagi4, and Bireshwar D. Mazumdar5

1 School of Computing Science and Engineering, Galgotias University, Greater Noida, Uttar Pradhesh, India

2 Department of Computer Science & Engineering, Greater Noida Institute of Technology, Greater Noida, Uttar Pradesh, India

3 Department of Industrial & Production Engineering, Institute of Engineering and Rural Technology (IERT), Allahabad, Uttar Pradesh, India

4 Department of Information Science and Engineering, Nagarjuna College of Engineering, Bengaluru, Karnataka, India

5 Department of Computer Science and Engineering, Faculty of Engineering and Technology, United University Prayagraj, Allahabad, Uttar Pradesh, India

1.1 Introduction

The ongoing Fourth Industrial Revolution (4IR) is characterized by the continuous transformation of society and the economy through technological advancements [1, 2]. This revolution encompasses breakthroughs in artificial intelligence (AI), robotics, the Internet of Things (IoT), and other digital technologies. What sets it apart from previous revolutions is not only the creation of new machines or processes but also the integration of these technologies into existing systems and the development of previously unimaginable systems [3].

The potential impacts of the 4IR on society and the economy are extensive and profound [4]. While these new technologies have the potential to improve productivity, efficiency, and quality of life for many, they also raise important questions regarding the equitable distribution of benefits and the potential exclusion of certain groups.

This chapter serves as an introduction to the 4IR and its societal impact. It explores the technological advancements driving this revolution, examines its potential effects on the economy and society, and addresses the ethical and governance considerations that arise in this era of technological progress.

The objective of this exploration is to deepen our understanding of the 4IR and its implications for society and the economy. Additionally, it explores how individuals, businesses, and governments can collaborate to shape this revolution in a way that maximizes its potential benefits while mitigating any negative consequences.

The 4IR refers to the current phase of technological advancements, encompassing AI, robotics, the IoT, and other digital technologies [5]. Coined by Klaus Schwab in his 2016 book The Fourth Industrial Revolution, it builds upon the transformative changes initiated by previous industrial revolutions [6].

The First Industrial Revolution introduced mechanization and steam power, while the Second Industrial Revolution brought electricity and mass production [7]. The Third Industrial Revolution, known as “the digital revolution,” introduced computers and digital technology. However, the 4IR is distinctive because it integrates and converges technologies across all aspects of life [8]. It blurs the boundaries between physical, digital, and biological systems, enabling unprecedented levels of automation, connectivity, and data analysis. This integration has the potential to revolutionize industries, boost productivity, and create new avenues for economic growth.

The anticipated impact of the 4IR on society and the economy is profound. It is crucial for us to understand its implications and foster collaboration to ensure the equitable distribution of its benefits.

1.1.1 Overview of the Major Technological Advancements Driving the Revolution

The ongoing 4IR is characterized by remarkable technological advancements that are profoundly reshaping our lifestyles and work dynamics [9]. These advancements include:

Artificial Intelligence: AI encompasses machines capable of performing tasks that traditionally require human intelligence, such as speech recognition, decision‐making, and experiential learning. It continues to evolve and finds applications in various fields, including autonomous vehicles, personalized medicine, and intelligent virtual assistants.

Robotics: Robotics involves the use of robots and automated systems to perform tasks that typically require human intervention. Advances in robotics enable increased levels of automation in industries such as manufacturing, logistics, and more.

Internet of Things: The IoT is a network that connects physical objects embedded with sensors, software, and other technologies, enabling data exchange and collection. It promotes connectivity and data analysis, leading to valuable insights and efficiencies in sectors such as health care, agriculture, and transportation.

Big Data Analytics: Big data refers to vast amounts of data generated by the IoT, social media, and other sources. Big data analytics involves employing advanced analytical techniques to extract insights and value from this data. It empowers organizations to make informed decisions and optimize their operations.

3D Printing: 3D printing involves the layer‐by‐layer creation of physical objects. Advances in 3D printing technology allow to produce intricate and precise objects, unlocking new possibilities in health care, aerospace, manufacturing, and other industries.

These advancements in the 4IR are revolutionizing various sectors and presenting exciting opportunities for innovation and growth (refer to Figure 1.1). They have the potential to reshape our society and economy in profound ways, driving us toward a more interconnected and technologically advanced future.

1.1.2 Importance of Studying and Understanding the Impacts of the Fourth Industrial Revolution

Studying and understanding the impacts of the 4IR is critical for several reasons [6, 10]:

Economic Growth: The 4IR has the potential to drive substantial economic growth by fostering innovation, creating new employment opportunities, and enhancing productivity. A deep understanding of the opportunities presented by these technological advancements enables businesses and governments to capitalize on them, leading to sustainable economic growth.

Social and Environmental Impact: The 4IR carries the potential for significant social and environmental consequences. It may exacerbate income inequality, contribute to job displacement through automation, and have adverse environmental effects. By comprehending the potential negative impacts of these technologies, we can develop policies and strategies to address and mitigate these challenges effectively.

Ethics and Governance: The 4IR raises crucial ethical and governance considerations. Privacy, security, and accountability become paramount concerns in a technologically advanced era. A thorough understanding of these considerations empowers us to establish ethical frameworks and governance structures that ensure responsible development and use of these technologies.

Education and Skills Development: The 4IR transforms the skill sets and educational requirements needed for the workforce of the future. Understanding the evolving demands and identifying the necessary skills equip us to prepare individuals and communities for the changing nature of work, fostering adaptability and lifelong learning.

Figure 1.1 Major technological advancements driving the revolution.

Figure 1.2 Importance of studying and understanding the impacts of the Fourth Industrial Revolution.

In summary, studying and comprehending the impacts of the 4IR are vital to optimize the benefits of these technological advancements while minimizing their negative consequences. By doing so, we can create a more equitable, sustainable, and prosperous future for all (refer to Figure 1.2).

1.2 The Technological Advancements of the Fourth Industrial Revolution

The 4IR represents a wave of transformative technological advancements that are fundamentally reshaping our lives and work dynamics [7, 11]. These advancements encompass a range of key technologies, which include the following:

Artificial Intelligence: AI enables machines to perform tasks that traditionally require human intelligence, such as speech recognition, decision‐making, and experiential learning. Ongoing advancements in AI have led to its application in various domains, including autonomous vehicles, personalized medicine, and intelligent virtual assistants.

Robotics: Robotics involves the use of robots and automated systems to perform tasks that typically require human intervention. Advances in robotics have facilitated increased levels of automation in industries such as manufacturing, logistics, and beyond.

Internet of Things: The IoT consists of a network of physical objects embedded with sensors, software, and other technologies for data collection and exchange. It enables enhanced connectivity and data analysis, leading to valuable insights and efficiencies in sectors like health care, agriculture, and transportation.

Big Data Analytics: Big data refers to vast amounts of data generated by the IoT, social media, and other sources. Big data analytics involves employing advanced techniques to extract insights and value from this data. Organizations leverage these insights to make informed decisions and optimize their operations.

3D Printing: 3D printing is an additive manufacturing process that constructs physical objects layer by layer. Advancements in 3D printing technology enable the production of intricate objects with exceptional precision, unlocking new possibilities in health care, aerospace, manufacturing, and more.

Blockchain: Blockchain is a distributed ledger technology that enables secure and transparent transactions without intermediaries. It has the potential to transform various industries, including finance, supply chain management, and real estate.

Augmented Reality (AR) and Virtual Reality (VR): AR and VR technologies provide immersive experiences that blend the physical and digital realms. These technologies find applications in fields such as education, entertainment, and retail.

These technological advancements are reshaping industries, creating new opportunities for growth. However, they also give rise to critical ethical and governance considerations. Understanding these advancements and their potential impact is crucial for individuals, businesses, and governments as we navigate the 4IR and strive to maximize its benefits while addressing its challenges.

1.2.1 Discussion of the Potential Benefits and Drawbacks of Each Technology

The key technologies driving the 4IR present both potential benefits and drawbacks [12]:

Artificial Intelligence

Benefits: AI has the potential to enhance decision‐making, improve efficiency, and enable better understanding of complex data. It offers applications in various fields, including health care and finance.

Drawbacks: Ethical concerns regarding bias and discrimination in AI decision‐making processes exist. There are also concerns about job displacement as AI and automation become more prevalent.

Robotics

Benefits: Robotics can increase efficiency, reduce costs, and improve safety in industries like manufacturing and logistics. It enables the completion of tasks that are dangerous or difficult for humans.

Drawbacks: Job displacement is a concern as robotics and automation advance. Safety and ethical considerations in the development and deployment of robots require attention.

3D Printing

Benefits: 3D printing enables faster, cheaper, and more customizable production of products. It has the potential to reduce waste and improve sustainability.

Drawbacks: 3D printing is still relatively costly compared to traditional manufacturing methods. The quality of printed products may not always meet the standards of traditional manufacturing.

Internet of Things

Advantages: The IoT offers real‐time insights and improved efficiency across industries. It enhances resource monitoring and management, promoting energy and water conservation.

Disadvantages: Security and privacy concerns arise as more devices become interconnected, necessitating robust data protection measures. Increased energy usage associated with the IoT raises environmental concerns.

Blockchain

Advantages: Blockchain enhances transparency, security, and efficiency in industries like finance, supply chain management, and real estate. It enables peer‐to‐peer transactions, reducing reliance on intermediaries.

Disadvantages: Energy consumption, especially with cryptocurrencies, poses environmental challenges. Scalability and interoperability of blockchain systems require attention.

Understanding the potential benefits and drawbacks of these technologies is crucial as we navigate the 4IR (refer to Figure 1.3). It allows for informed decision‐making regarding their development and deployment, enabling us to harness their advantages while addressing the associated challenges.

1.2.2 Examples of How These Technologies Are Already Being Used in Various Industries

The key technologies of the 4IR are already being applied in various industries, leading to transformative outcomes [12]:

Artificial Intelligence

Health Care: AI assists in disease diagnosis, treatment development, and improving patient outcomes. It analyzes medical images, predicts high‐risk individuals, and enables proactive interventions.

Finance: AI enhances fraud detection, risk management, and customer service in the financial industry. AI‐powered chatbots offer personalized financial advice and efficient customer support.

Robotics

Manufacturing: Robotics automates manufacturing processes, increasing efficiency and reducing costs. Robots assemble products, handle materials, and perform quality control checks.

Health Care: Robotics assists in surgeries and medical procedures, enabling precision and reducing risks.

3D Printing

Manufacturing: 3D printing creates prototypes, custom parts, and complete products. Automotive companies use it to produce lightweight, high‐performance vehicle components.

Health Care: 3D printing creates customized prosthetics, implants, and surgical instruments, improving patient outcomes and cost‐effectiveness.

Internet of Things

Agriculture: IoT sensors monitor soil moisture, temperature, and environmental factors, optimizing crop yields while conserving water resources.

Transportation: IoT sensors enable vehicle performance monitoring, inventory tracking, and route optimization in logistics, enhancing efficiency and reducing costs.

Blockchain

Finance: Blockchain ensures secure and transparent transactions, facilitating peer‐to‐peer payments and cross‐border transfers, enhancing efficiency and transparency in international money transfers.

Supply Chain Management: Blockchain improves transparency and efficiency by enabling reliable tracking and verification of goods throughout the supply chain.

Figure 1.3 Potential benefits.

These examples illustrate how these technologies are already being utilized in various industries. Their potential applications are extensive and diverse, promising further advancements and transformations.

1.3 Impacts on the Economy

The 4IR has the potential to bring significant impacts to the global economy, as indicated by research [13, 14]. Consider the following key aspects:

Enhanced Productivity: The integration of automation, robotics, and AI in industries like manufacturing, transportation, and logistics can boost productivity and efficiency. This can lead to reduced production costs and improved profitability for businesses.

Job Transformation: While certain industries may experience job displacement due to technological advancements, new job opportunities can arise in other sectors. For instance, the implementation of AI in customer service may reduce the need for human operators while creating new roles in AI development and maintenance.

Shift in Skills: The increasing prevalence of automation and AI across industries may require workers to acquire new skills. Proficiency in programming, data analysis, and other tech‐related skills can become crucial for individuals to remain competitive in the evolving job market.

Heightened Global Competition: The 4IR can foster increased global competition as companies leverage technology to enhance their products and services. This drive for innovation and efficiency can bring benefits, but it may also intensify competition for jobs and market share.

Disruption of Business Models: New technologies have the potential to disrupt traditional business models. For example, the rise of e‐commerce has disrupted brick‐and‐mortar retail, leading to store closures, while simultaneously creating opportunities in online retailing.

Figure 1.4 Impacts on the economy.

In summary, the 4IR offers significant economic benefits (refer to Figure 1.4), but it also presents challenges and disruptions. Governments, businesses, and individuals should comprehend these potential impacts to effectively navigate this transformative era. Collaboration is vital to maximize the benefits while mitigating any negative effects.

1.3.1 The Potential Impacts of the Fourth Industrial Revolution on the Economy

The 4IR has the potential to significantly impact the economy, including changes in employment, productivity, and industry structure [15, 16]. Consider the following key points:

Employment Changes: The 4IR is expected to result in the displacement of jobs that are repetitive or routine, particularly in sectors like manufacturing and transportation. However, it can also create new opportunities in emerging fields such as data analysis, AI, and robotics. Acquiring specialized skills will be crucial to capitalize on these new roles, potentially leading to a skills gap and limited opportunities for workers without these skills.

Increased Productivity: Automation, AI, and robotics are anticipated to enhance productivity in various industries, particularly manufacturing, logistics, and transportation. This can lead to lower production costs and increased profitability for businesses.

Industry Structure: The 4IR has the potential to disrupt traditional industry structures and give rise to new business models. For example, e‐commerce has disrupted brick‐and‐mortar retail, while ride‐sharing platforms have transformed transportation. These disruptions may reshape industry structures and introduce new players across multiple sectors.

Heightened Competition: The 4IR is projected to intensify global competition as companies leverage technology to improve their products and services. This can drive innovation and efficiency but may present challenges for smaller businesses and potentially lead to industry consolidation.

Shift in Skill Requirements: The 4IR will require workers to possess new and specialized skills, particularly in areas such as data analysis, AI, and robotics. This shift in skill requirements may create a skills gap and limit employment opportunities for individuals lacking these proficiencies. However, it also presents an opportunity for individuals to acquire new skills and remain competitive in the evolving job market.

Figure 1.5 Potential impacts of the Fourth Industrial Revolution on the economy.

In summary, the 4IR has the potential to bring significant transformations to the economy (refer to Figure 1.5). It is crucial for businesses, governments, and individuals to understand and adapt to these potential impacts by investing in education and training, as well as implementing policies and regulations that ensure a fair and equitable distribution of the benefits generated by the 4IR across society.

1.3.2 Analysis of Fourth Industrial Revolution Is Changing the Nature of Work and Engages in Economic Activities

The 4IR is bringing about significant transformations in work and economic activities, resulting in various implications [17, 18]. Consider the following examples:

Automation: The integration of automation, robotics, and AI is automating routine and repetitive tasks, particularly in manufacturing and logistics. While this may lead to the displacement of low‐skill or repetitive jobs, it also creates new employment opportunities in fields such as data analysis and AI development.

Gig Economy: The 4IR has fueled the growth of the gig economy, allowing individuals to engage in freelance or contract work through digital platforms. While it offers flexibility, it also presents challenges such as job insecurity and limited benefits.

Remote Work: Technological advancements have made remote work more feasible, with the COVID‐19 pandemic further accelerating its adoption. Remote work offers flexibility and reduces commuting time, but it also brings challenges like social isolation and managing work‐life balance.

Skill Requirements: The 4IR demands new and specialized skills, particularly in areas such as data analysis, AI, and robotics. This may result in a skills gap, limiting employment opportunities for individuals without these specific skills.

New Business Models: The 4IR has enabled the emergence of innovative business models like the sharing economy and subscription‐based services. While these models create new opportunities for workers and consumers, they also disrupt traditional industries and pose challenges for businesses that fail to adapt.

Figure 1.6 Fourth Industrial Revolution.

In summary, the 4IR is reshaping work and economic activities in various ways (refer to Figure 1.6). While it offers flexibility and efficiency, it also presents challenges such as job displacement, evolving skill requirements, and the need to adapt to new business models. It is crucial for businesses, governments, and individuals to understand and embrace these changes to ensure that the benefits of the 4IR are equitably shared across society.

1.3.3 Discussion of How Businesses and Governments Can Prepare for and Adapt to These Changes

To effectively prepare for and adapt to the transformative effects of the 4IR [19, 20], businesses and governments can undertake several proactive measures. Consider the following examples:

Invest in Training and Education: Businesses can allocate resources to training programs that enhance the skills of their workforce in areas such as data analysis, AI, and robotics. Simultaneously, governments can invest in education and training initiatives to equip individuals with the necessary skills demanded by the evolving job market.

Foster Innovation: Businesses can foster innovation by dedicating resources to research and development, collaborating with startups and other innovative entities, and exploring new business models. Governments can support innovation by implementing policies that encourage it, such as providing tax incentives and research grants.

Promote Entrepreneurship: Governments can establish policies that promote entrepreneurship and offer support to small businesses, including access to funding and streamlined regulatory processes. This can stimulate the emergence of new enterprises and industries in response to the evolving economic landscape.

Embrace Digital Transformation: Businesses can embrace digital transformation by adopting new technologies and business models, such as those found in the sharing economy and subscription‐based services. Governments can facilitate digital transformation by investing in digital infrastructure and creating regulatory frameworks that foster innovation and digitalization.

Address Social and Economic Inequalities: The 4IR has the potential to exacerbate social and economic disparities. To tackle this, businesses and governments can invest in programs that provide training and education opportunities to underserved communities, support initiatives that promote diversity and inclusion, and implement policies to assist workers displaced by automation.

In summary, collaboration between businesses and governments is crucial to effectively prepare for and adapt to the changes brought about by the 4IR. By investing in education and training, fostering innovation, promoting entrepreneurship, embracing digital transformation, and addressing social and economic inequalities, they can ensure that the benefits of the 4IR are distributed equitably throughout society.

1.4 Impacts on Society

The 4IR is anticipated to have profound impacts on society, as suggested by various studies [21, 22]. Here are some potential impacts to consider:

Increased Connectivity: The 4IR has the capacity to enhance connectivity among individuals, communities, and nations. This can foster the exchange of information, ideas, and resources, promoting global cooperation and collaboration.

Changes in Social Structures: The 4IR may bring about changes in social structures, influencing how people work, socialize, and interact with one another. For instance, remote work and the sharing economy could reshape the traditional employer‐employee relationship, while social media and VR may alter the dynamics of socialization and relationship‐building.

Disruption of Existing Industries: The 4IR has the potential to disrupt established industries, leading to workforce displacement and shifts in the economy. Automation and robotics could replace human workers in certain sectors, while 3D printing might disrupt traditional manufacturing processes.

New Economic Opportunities: The 4IR is also expected to create fresh economic opportunities, such as the emergence of new industries and jobs in fields like AI, robotics, and biotechnology. This could contribute to economic growth and the generation of employment opportunities.